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library(velocyto.R)
载入需要的程辑包:Matrix
library(SeuratWrappers)
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library(Seurat)

载入程辑包:‘Seurat’

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    ALRAChooseKPlot, ExportToCellbrowser, ReadAlevin, RunALRA, StopCellbrowser
source("tianfengRwrappers.R")
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clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
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========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

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Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

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========================================

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========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


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AC

#首先需要取并集,然后使用已有的umap,在其上绘制速率图
## AC
velodata1 <- ReadVelocity(file ="./velo_data/CARconHEL.loom")
reading loom file via hdf5r...
velodata2 <- ReadVelocity(file ="./velo_data/CARconHEA0620.loom")
reading loom file via hdf5r...
velodata3 <- ReadVelocity(file ="./velo_data/CARconHEA0717.loom")
reading loom file via hdf5r...
## PA
# velodata1 <- ReadVelocity(file ="./velo_data/CARconDIS.loom")
# velodata2 <- ReadVelocity(file ="./velo_data/CARconDIS0620.loom")
# velodata3 <- ReadVelocity(file ="./velo_data/CARconDIS0717.loom")
#匹配两次的barcode
func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"1",sep = "-")
  return(s)
}

velodata1[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["spliced"]]@Dimnames[[2]],func))
velodata1[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["unspliced"]]@Dimnames[[2]],func))
velodata1[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata1[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"3",sep = "-")
  return(s)
}

velodata2[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["spliced"]]@Dimnames[[2]],func))
velodata2[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["unspliced"]]@Dimnames[[2]],func))
velodata2[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata2[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"5",sep = "-")
  return(s)
}

velodata3[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["spliced"]]@Dimnames[[2]],func))
velodata3[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["unspliced"]]@Dimnames[[2]],func))
velodata3[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata3[["ambiguous"]]@Dimnames[[2]],func))
# CA_dataset2 <- readRDS("CA_dataset2.rds")
ds2_AC <- readRDS("ds2_AC.rds")
sp1_velo <- as.Seurat(x = velodata1)
sp2_velo <- as.Seurat(x = velodata2)
sp3_velo <- as.Seurat(x = velodata3)
merge_velo <- merge(sp1_velo, c(sp2_velo,sp3_velo))
merge_velo <- subset(merge_velo, cells = WhichCells(ds2_AC))
rm(sp1_velo)
rm(sp2_velo)
rm(sp3_velo)
rm(velodata1)
rm(velodata2)
rm(velodata3)

merge_velo <- readRDS("dataset2_PA_velo.RDS")
Loading required package: Seurat
Registered S3 method overwritten by 'data.table':
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# saveRDS(merge_velo,"dataset2_AC_velo.RDS")
# merge_velo <- readRDS("dataset2_AC_velo.RDS")
ident.colors <- colors_list[1:length(x = levels(x = merge_velo))]
names(x = ident.colors) <- levels(x = merge_velo)
cell.colors <- ident.colors[Idents(object = merge_velo)]

names(x = cell.colors) <- colnames(x = merge_velo)
png("velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = merge_velo, reduction = "umap"), vel = Tool(object = merge_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
delta projections ... sqrt knn ... transition probs ... done
calculating arrows ... done
grid estimates ... grid.sd= 0.5051766  min.arrow.size= 0.01010353  max.grid.arrow.length= 0.04539128  done
dev.off()
null device 
          1 
# plot <- tSNE.velocity.plot(vel = Tool(object = sp1_velo, slot = "RunVelocity"))
source("../vascular-analysis/seuratToAnnDataCombined.R")
h5ad_output <- "AC_SMC_velo.h5ad"  

library(reticulate)
library(Matrix)
writeMM(t(merge_velo@assays$RNA@counts), file='combined.mtx')
NULL
writeMM(t(merge_velo@assays$spliced@counts), file='spliced.mtx')
NULL
writeMM(t(merge_velo@assays$unspliced@counts), file='unspliced.mtx')
NULL
write.csv(rownames(merge_velo@assays$spliced@counts), file = "genes.csv", row.names = FALSE)
write.csv(merge_velo@reductions$umap@cell.embeddings, file = "umap.csv", row.names = FALSE)
write.csv(merge_velo@reductions$pca@cell.embeddings, file = "pca.csv", row.names = FALSE)

write.csv(colnames(merge_velo@assays$spliced@counts), file = "cells.csv", row.names = FALSE)
write.csv(merge_velo@meta.data, file = "meta.csv", row.names = FALSE)
source_python('~/scRNAseq/vascular-analysis/build.py')
build(h5ad_output, pca = TRUE, umap = TRUE)
file.remove('combined.mtx')
[1] TRUE
file.remove('spliced.mtx')
[1] TRUE
file.remove('unspliced.mtx')
[1] TRUE
file.remove('genes.csv')
[1] TRUE
file.remove('cells.csv')
[1] TRUE
file.remove('umap.csv')
[1] TRUE
file.remove('pca.csv')
[1] TRUE
file.remove('meta.csv')
[1] TRUE

PA

velodata1 <- ReadVelocity(file ="./velo_data/CARconDIS.loom")
reading loom file via hdf5r...
velodata2 <- ReadVelocity(file ="./velo_data/CARconDIS0620.loom")
reading loom file via hdf5r...
velodata3 <- ReadVelocity(file ="./velo_data/CARconDIS0717.loom")
reading loom file via hdf5r...
#匹配两次的barcode
func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"2",sep = "-")
  return(s)
}

velodata1[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["spliced"]]@Dimnames[[2]],func))
velodata1[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["unspliced"]]@Dimnames[[2]],func))
velodata1[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata1[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"4",sep = "-")
  return(s)
}

velodata2[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["spliced"]]@Dimnames[[2]],func))
velodata2[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["unspliced"]]@Dimnames[[2]],func))
gc()
             used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells    9626592  514.2   17054862   910.9   17054862   910.9
Vcells 1092743890 8337.0 2184824865 16668.9 2127190354 16229.2
velodata2[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata2[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"6",sep = "-")
  return(s)
}

velodata3[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["spliced"]]@Dimnames[[2]],func))
velodata3[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["unspliced"]]@Dimnames[[2]],func))
velodata3[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata3[["ambiguous"]]@Dimnames[[2]],func))
# ds2_PA <- readRDS("ds2_PA.rds")
sp1_velo <- as.Seurat(x = velodata1)
sp2_velo <- as.Seurat(x = velodata2)
sp3_velo <- as.Seurat(x = velodata3)
merge_velo <- merge(sp1_velo, c(sp2_velo,sp3_velo))
merge_velo <- subset(merge_velo, cells = WhichCells(ds2_PA))
rm(sp1_velo)
rm(sp2_velo)
rm(sp3_velo)
rm(velodata1)
rm(velodata2)
rm(velodata3)
Idents(merge_velo) <- merge_velo$Classification1
错误: Cannot find 'Classification1' in this Seurat object
h5ad_output <- "PA_SMC_velo.h5ad"  

library(reticulate)
library(Matrix)
writeMM(t(merge_velo@assays$RNA@counts), file='combined.mtx')
NULL
writeMM(t(merge_velo@assays$spliced@counts), file='spliced.mtx')
NULL
writeMM(t(merge_velo@assays$unspliced@counts), file='unspliced.mtx')
NULL
write.csv(rownames(merge_velo@assays$spliced@counts), file = "genes.csv", row.names = FALSE)
write.csv(merge_velo@reductions$umap@cell.embeddings, file = "umap.csv", row.names = FALSE)
write.csv(merge_velo@reductions$pca@cell.embeddings, file = "pca.csv", row.names = FALSE)

write.csv(colnames(merge_velo@assays$spliced@counts), file = "cells.csv", row.names = FALSE)
write.csv(merge_velo@meta.data, file = "meta.csv", row.names = FALSE)
source_python('~/scRNAseq/vascular-analysis/build.py')
build(h5ad_output, pca = TRUE, umap = TRUE)
file.remove('combined.mtx')
[1] TRUE
file.remove('spliced.mtx')
[1] TRUE
file.remove('unspliced.mtx')
[1] TRUE
file.remove('genes.csv')
[1] TRUE
file.remove('cells.csv')
[1] TRUE
file.remove('umap.csv')
[1] TRUE
file.remove('pca.csv')
[1] TRUE
file.remove('meta.csv')
[1] TRUE

只在SMC亚群中进行

SMC_velo <- subset(merge_velo, idents = "SMC")
RunVelocity(object = SMC_velo, deltaT = 1, kCells = 25, fit.quantile = 0.02)
ident.colors <- colors_list[1:length(x = levels(x = SMC_velo))]
names(x = ident.colors) <- levels(x = SMC_velo)
cell.colors <- ident.colors[Idents(object = SMC_velo)]

names(x = cell.colors) <- colnames(x = SMC_velo)
png("SMC_velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = SMC_velo, reduction = "umap"), vel = Tool(object = SMC_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
dev.off()

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---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(velocyto.R)
library(SeuratWrappers)
library(Seurat)
source("tianfengRwrappers.R")

```
## AC
```{r}
#首先需要取并集，然后使用已有的umap，在其上绘制速率图

velodata1 <- ReadVelocity(file ="./velo_data/CARconHEL.loom")
velodata2 <- ReadVelocity(file ="./velo_data/CARconHEA0620.loom")
velodata3 <- ReadVelocity(file ="./velo_data/CARconHEA0717.loom")
```


```{r}
#匹配两次的barcode
func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"1",sep = "-")
  return(s)
}

velodata1[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["spliced"]]@Dimnames[[2]],func))
velodata1[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["unspliced"]]@Dimnames[[2]],func))
velodata1[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata1[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"3",sep = "-")
  return(s)
}

velodata2[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["spliced"]]@Dimnames[[2]],func))
velodata2[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["unspliced"]]@Dimnames[[2]],func))
velodata2[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata2[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"5",sep = "-")
  return(s)
}

velodata3[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["spliced"]]@Dimnames[[2]],func))
velodata3[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["unspliced"]]@Dimnames[[2]],func))
velodata3[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata3[["ambiguous"]]@Dimnames[[2]],func))
```


```{r}
# CA_dataset2 <- readRDS("CA_dataset2.rds")
ds2_AC <- readRDS("ds2_AC.rds")
sp1_velo <- as.Seurat(x = velodata1)
sp2_velo <- as.Seurat(x = velodata2)
sp3_velo <- as.Seurat(x = velodata3)
merge_velo <- merge(sp1_velo, c(sp2_velo,sp3_velo))
merge_velo <- subset(merge_velo, cells = WhichCells(ds2_AC))
rm(sp1_velo)
rm(sp2_velo)
rm(sp3_velo)
rm(velodata1)
rm(velodata2)
rm(velodata3)
```


```{r}
merge_velo <- merge_velo %>% 
    PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F,assay = "spliced") %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

VlnPlot(merge_velo,"percent.mt",group.by = "orig.ident")
VlnPlot(merge_velo, features = c("nFeature_spliced", "nCount_spliced"), ncol = 2,group.by = "orig.ident")

DimPlot(merge_velo, reduction = "umap",pt.size = 0.5,label = T)

merge_velo@reductions[["umap"]] <- ds2_AC@reductions[["umap"]]
merge_velo@reductions[["umap"]]@cell.embeddings <-  ds2_AC@reductions[["umap"]]@cell.embeddings[colnames(merge_velo),]

DimPlot(merge_velo, reduction = "umap",pt.size = 0.5,label = T)
merge_velo$Classification1 <- Idents(ds2_AC)
Idents(merge_velo) <- merge_velo$Classification1
merge_velo@assays[["RNA"]] <- ds2_AC@assays[["RNA"]]

f("CNN1",merge_velo)

```


```{r fig.width=6,fig.height=6}
# merge_velo <- FindClusters(merge_velo, resolution = 0.1)
# umapplot(merge_velo)
# multi_featureplot(c("LYZ","PTPRC","CD69","EPCAM","CDH1","PDGFRB","COL1A2","PECAM1","CLDN5"),merge_velo)


merge_velo@assays[["RNA"]] <- CA_dataset2@assays[["RNA"]]

saveRDS(merge_velo,"ds2_AC_velo.rds")
```


```{r}
merge_velo <- readRDS("dataset2_PA_velo.rds")
merge_velo <- RunVelocity(object = merge_velo, deltaT = 1, kCells = 25, fit.quantile = 0.02, ncores = 3) # fitting gamma coefficients 用时长
```


```{r}
# saveRDS(merge_velo,"dataset2_AC_velo.RDS")
# merge_velo <- readRDS("dataset2_AC_velo.RDS")
ident.colors <- colors_list[1:length(x = levels(x = merge_velo))]
names(x = ident.colors) <- levels(x = merge_velo)
cell.colors <- ident.colors[Idents(object = merge_velo)]

names(x = cell.colors) <- colnames(x = merge_velo)
png("PAvelocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = merge_velo, reduction = "umap"), vel = Tool(object = merge_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
dev.off()

# plot <- tSNE.velocity.plot(vel = Tool(object = sp1_velo, slot = "RunVelocity"))
```


```{r}
source("../vascular-analysis/seuratToAnnDataCombined.R")
```

```{r}
h5ad_output <- "AC_SMC_velo.h5ad"  

library(reticulate)
library(Matrix)
writeMM(t(merge_velo@assays$RNA@counts), file='combined.mtx')
writeMM(t(merge_velo@assays$spliced@counts), file='spliced.mtx')
writeMM(t(merge_velo@assays$unspliced@counts), file='unspliced.mtx')
write.csv(rownames(merge_velo@assays$spliced@counts), file = "genes.csv", row.names = FALSE)
write.csv(merge_velo@reductions$umap@cell.embeddings, file = "umap.csv", row.names = FALSE)
write.csv(merge_velo@reductions$pca@cell.embeddings, file = "pca.csv", row.names = FALSE)

write.csv(colnames(merge_velo@assays$spliced@counts), file = "cells.csv", row.names = FALSE)
write.csv(merge_velo@meta.data, file = "meta.csv", row.names = FALSE)
source_python('~/scRNAseq/vascular-analysis/build.py')
build(h5ad_output, pca = TRUE, umap = TRUE)
file.remove('combined.mtx')
file.remove('spliced.mtx')
file.remove('unspliced.mtx')
file.remove('genes.csv')
file.remove('cells.csv')
file.remove('umap.csv')
file.remove('pca.csv')
file.remove('meta.csv')
```
# PA
```{r}
velodata1 <- ReadVelocity(file ="./velo_data/CARconDIS.loom")
velodata2 <- ReadVelocity(file ="./velo_data/CARconDIS0620.loom")
velodata3 <- ReadVelocity(file ="./velo_data/CARconDIS0717.loom")
```


```{r}
#匹配两次的barcode
func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"2",sep = "-")
  return(s)
}

velodata1[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["spliced"]]@Dimnames[[2]],func))
velodata1[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["unspliced"]]@Dimnames[[2]],func))
velodata1[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata1[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"4",sep = "-")
  return(s)
}

velodata2[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["spliced"]]@Dimnames[[2]],func))
velodata2[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["unspliced"]]@Dimnames[[2]],func))
velodata2[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata2[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"6",sep = "-")
  return(s)
}

velodata3[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["spliced"]]@Dimnames[[2]],func))
velodata3[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["unspliced"]]@Dimnames[[2]],func))
velodata3[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata3[["ambiguous"]]@Dimnames[[2]],func))
```


```{r}
# ds2_PA <- readRDS("ds2_PA.rds")
sp1_velo <- as.Seurat(x = velodata1)
sp2_velo <- as.Seurat(x = velodata2)
sp3_velo <- as.Seurat(x = velodata3)
merge_velo <- merge(sp1_velo, c(sp2_velo,sp3_velo))
merge_velo <- subset(merge_velo, cells = WhichCells(ds2_PA))
rm(sp1_velo)
rm(sp2_velo)
rm(sp3_velo)
rm(velodata1)
rm(velodata2)
rm(velodata3)
```


```{r}
merge_velo <- merge_velo %>% 
    PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F,assay = "spliced") %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

VlnPlot(merge_velo,"percent.mt",group.by = "orig.ident")
VlnPlot(merge_velo, features = c("nFeature_spliced", "nCount_spliced"), ncol = 2,group.by = "orig.ident")

DimPlot(merge_velo, reduction = "umap",pt.size = 0.5,label = T)

merge_velo@reductions[["umap"]] <- ds2_PA@reductions[["umap"]]
merge_velo@reductions[["umap"]]@cell.embeddings <-  ds2_PA@reductions[["umap"]]@cell.embeddings[colnames(merge_velo),]

DimPlot(merge_velo, reduction = "umap",pt.size = 0.5,label = T)
merge_velo$Classification1 <- Idents(ds2_PA)
Idents(merge_velo) <- merge_velo$Classification1
merge_velo@assays[["RNA"]] <- ds2_PA@assays[["RNA"]]
saveRDS(merge_velo,"ds2_PA_velo.rds")
```

```{r}
h5ad_output <- "PA_SMC_velo.h5ad"  

library(reticulate)
library(Matrix)
writeMM(t(merge_velo@assays$RNA@counts), file='combined.mtx')
writeMM(t(merge_velo@assays$spliced@counts), file='spliced.mtx')
writeMM(t(merge_velo@assays$unspliced@counts), file='unspliced.mtx')
write.csv(rownames(merge_velo@assays$spliced@counts), file = "genes.csv", row.names = FALSE)
write.csv(merge_velo@reductions$umap@cell.embeddings, file = "umap.csv", row.names = FALSE)
write.csv(merge_velo@reductions$pca@cell.embeddings, file = "pca.csv", row.names = FALSE)

write.csv(colnames(merge_velo@assays$spliced@counts), file = "cells.csv", row.names = FALSE)
write.csv(merge_velo@meta.data, file = "meta.csv", row.names = FALSE)
source_python('~/scRNAseq/vascular-analysis/build.py')
build(h5ad_output, pca = TRUE, umap = TRUE)
file.remove('combined.mtx')
file.remove('spliced.mtx')
file.remove('unspliced.mtx')
file.remove('genes.csv')
file.remove('cells.csv')
file.remove('umap.csv')
file.remove('pca.csv')
file.remove('meta.csv')
```


# 只在SMC亚群中进行
```{r}
SMC_velo <- subset(merge_velo, idents = "SMC")
RunVelocity(object = SMC_velo, deltaT = 1, kCells = 25, fit.quantile = 0.02)
ident.colors <- colors_list[1:length(x = levels(x = SMC_velo))]
names(x = ident.colors) <- levels(x = SMC_velo)
cell.colors <- ident.colors[Idents(object = SMC_velo)]

names(x = cell.colors) <- colnames(x = SMC_velo)
png("SMC_velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = SMC_velo, reduction = "umap"), vel = Tool(object = SMC_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
dev.off()
```


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